Competitive Belief Propagation to Efficiently Solve Complex Multi-agent Negotiations with Network Structure

2017 ◽  
pp. 1-16
Author(s):  
Ivan Marsa-Maestre ◽  
Jose Manuel Gimenez-Guzman ◽  
Enrique de la Hoz ◽  
David Orden
Keyword(s):  
Network Structure ◽  
Belief Propagation ◽  
Multi Agent

scholarly journals FAULT-TOLERANT MULTI-AGENT EXACT BELIEF PROPAGATION

2009 ◽  
Vol 25 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Xiangdong An ◽  
Nick Cercone
Keyword(s):  
Belief Propagation ◽  
Fault Tolerant ◽  
Multi Agent

scholarly journals The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment

2021 ◽  
Author(s):  
James Benjamin Falandays ◽  
Paul E. Smaldino
Keyword(s):  
Network Structure ◽  
Cultural Evolution ◽  
Category Learning ◽  
Cognitive Model ◽  
Individual Experience ◽  
Agent Based ◽  
Attractor Dynamics ◽  
Level Culture ◽  
Multi Agent ◽  
Dynamic Populations

Cultural attractor landscapes describe the time-evolution of cultural variants (i.e. behaviors, artifacts) over successive transmission events. Because cultural attractors are emergent products of dynamic populations of \textit{cognitive} landscapes, which are in turn emergent products of individual experience within a culture, stable cultural attractor landscapes cannot be taken for granted. Yet, little is known about how cultural attractors form, change, or stabilize. We present an agent-based model of cultural attractor dynamics, which adapts a cognitive model of unsupervised category learning to a multi-agent sociocultural setting wherein individual learners provide the training input to each other. We highlight three interesting behaviors exhibited by our model that are not accounted for in other models of cultural evolution: First, we find that some noise is beneficial to stabilizing cognitive alignment. Second, we find that long learning times may destabilize and limit the complexity of cultural repertoires, while critical or sensitive periods of learning enhance stability. Third, we find that larger populations develop less complex, but more stable patterns of alignment, and that this effect can be moderated by network structure. These results suggest that additional complexity may be needed in models of cultural evolution to adequately understand how human-level culture develops.

A Simulation of Temporally Variant Agent Interaction via Belief Promulgation

2012 ◽  
pp. 913-927
Author(s):  
Adam J. Conover
Keyword(s):  
Message Passing ◽  
Belief Propagation ◽  
Autonomous Agent ◽  
Emergent Properties ◽  
Agent Behavior ◽  
Temporal Variance ◽  
Agent Interaction ◽  
Multi Agent ◽  
Asynchronous Environments ◽  
Global Population

This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous” environments. Many traditional agent and swarm simulation environments divide time into discrete “ticks” where all entity behavior is synchronized to a master “world clock”. In other words, all agent behavior is governed by a single timer where all agents act and interact within deterministic time intervals. This discrete timing mechanism produces a somewhat restricted and artificial model of autonomous agent interaction. In addition to the behavioral autonomy normally associated with agents, simulated agents should also have “temporal autonomy” in order to interact realistically. This chapter focuses on the exploration of a grid of specially embedded, message-passing agents, where each message represents the communication of a core “belief”. Here, we focus our attention on the how the temporal variance of belief propagation from individual agents induces emergent and dynamic effects on a global population.

scholarly journals The evolution of Lexical Usage Profiles in social networks

2016 ◽  
Vol 30 ◽  
pp. 193-217
Author(s):  
Gerhard Schaden
Keyword(s):  
Social Networks ◽  
Reinforcement Learning ◽  
Network Structure ◽  
Usage Patterns ◽  
Complete Networks ◽  
Multi Agent

This paper investigates how network structure influences the outcomes of reinforcement learning in a series of multi-agent simulations. Its basic results are the following: (i) contact between agents in networks creates similarity in the usage patterns of the signals these agents use; (ii) in case of complete networks, the bigger the network, the smaller the lexical differentiation; and (iii) in networks consisting of linked cliques, the distance between usage patterns reflects on average the structure of the network.

scholarly journals The Impact of Network Structure on the Perturbation Dynamics of a Multi-agent Economic Model

2012 ◽  
pp. 331-338 ◽  
Author(s):  
Marshall A. Kuypers ◽  
Walter E. Beyeler ◽  
Robert J. Glass ◽  
Matthew Antognoli ◽  
Michael D. Mitchell
Keyword(s):  
Network Structure ◽  
Economic Model ◽  
Multi Agent ◽  
The Impact

A Simulation of Temporally Variant Agent Interaction via Belief Promulgation

2009 ◽  
pp. 261-274
Author(s):  
Adam J. Conover
Keyword(s):  
Message Passing ◽  
Belief Propagation ◽  
Autonomous Agent ◽  
Emergent Properties ◽  
Agent Behavior ◽  
Temporal Variance ◽  
Agent Interaction ◽  
Multi Agent ◽  
Asynchronous Environments ◽  
Global Population

This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous” environments. Many traditional agent and swarm simulation environments divide time into discrete “ticks” where all entity behavior is synchronized to a master “world clock”. In other words, all agent behavior is governed by a single timer where all agents act and interact within deterministic time intervals. This discrete timing mechanism produces a somewhat restricted and artificial model of autonomous agent interaction. In addition to the behavioral autonomy normally associated with agents, simulated agents should also have “temporal autonomy” in order to interact realistically. This chapter focuses on the exploration of a grid of specially embedded, message-passing agents, where each message represents the communication of a core “belief”. Here, we focus our attention on the how the temporal variance of belief propagation from individual agents induces emergent and dynamic effects on a global population.

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